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This is a Template course for Meta-data XML
1.Machine Learning:
2.Machine Learning: as a Tool for Classifying Patterns
3.Old Philosophical Debates
4.Machine Learning Viewpoint
3.Old Philosophical Debates
4.Machine Learning Viewpoint
5.Patterns as Conglomerations of sample Points
6.ML Viewpoint (Cnt・d)
7.Models
8.Neural Networks
9.Back-Propagation Neural Networks
10.Illustration
11.Support Vector Machines (SVM)
10.Illustration
9.Back-Propagation Neural Networks
10.Illustration
11.Support Vector Machines (SVM)
12.SVM
13.Illustration
14.Classification and Regression Tree (CART)
13.Illustration
12.SVM
13.Illustration
14.Classification and Regression Tree (CART)
13.Illustration
12.SVM
13.Illustration
14.Classification and Regression Tree (CART)
13.Illustration
12.SVM
11.Support Vector Machines (SVM)
12.SVM
13.Illustration
14.Classification and Regression Tree (CART)
15.slide 15
16.AdaBoost
15.slide 15
16.AdaBoost
17.AdaBoost
18.Statistical Models
19.Bayesian Approach
18.Statistical Models
17.AdaBoost
18.Statistical Models
19.Bayesian Approach
18.Statistical Models
19.Bayesian Approach
1.Machine Learning:
1.Machine Learning:
2.Machine Learning: as a Tool for Classifying Patterns
3.Old Philosophical Debates
5.Patterns as Conglomerations of sample Points
6.ML Viewpoint (Cnt・d)
7.Models
8.Neural Networks
9.Back-Propagation Neural Networks
10.Illustration
11.Support Vector Machines (SVM)
12.SVM
13.Illustration
14.Classification and Regression Tree (CART)
15.slide 15
16.AdaBoost
18.Statistical Models
19.Bayesian Approach
20.Bayesian Approach (Cnt・d)
21.A Bayesian Model with Hidden Variables
20.Bayesian Approach (Cnt・d)
21.A Bayesian Model with Hidden Variables
22.Hidden Markov Model (HMM)
23.Boltzmann-Gibbs Distribution
24.Boltzmann-Gibbs (Cnt・d)
23.Boltzmann-Gibbs Distribution
24.Boltzmann-Gibbs (Cnt・d)
25.Boltzmann-Gibbs (Cnt・d)
24.Boltzmann-Gibbs (Cnt・d)
25.Boltzmann-Gibbs (Cnt・d)
26.Boltzmann-Gibbs (Cnt・d)
27.References